class_docstr: Class for the Fisher information metric on beta distributions.
class_docstr_tok: ["Class","for","the","Fisher","information","metric","on","beta","distributions","."]
docstr: Compute the determinant of the metric.
  
  Parameters
  ----------
  point : array-like, shape=[..., 2]
      Point representing a beta distribution.
  
  
  Returns
  -------
  metric_det : array-like, shape=[...,]
      Determinant of the metric.
docstr_tok: ["Compute","the","determinant","of","the","metric",".","Parameters","--","--","--","--","--","point",":","array-like",",","shape=","[","...",",","2","]","Point","representing","a","beta","distribution",".","Returns","--","--","--","-","metric_det",":","array-like",",","shape=","[","...",",","]","Determinant","of","the","metric","."]
code: def metric_det(point):
      """Compute the determinant of the metric.
  
      Parameters
      ----------
      point : array-like, shape=[..., 2]
          Point representing a beta distribution.
  
  
      Returns
      -------
      metric_det : array-like, shape=[...,]
          Determinant of the metric.
      """
      param_a = point[..., 0]
      param_b = point[..., 1]
      metric_det = gs.polygamma(1, param_a) * gs.polygamma(
          1, param_b
      ) - gs.polygamma(1, param_a + param_b) * (
          gs.polygamma(1, param_a) + gs.polygamma(1, param_b)
      )
      return metric_det
  
file_path: geomstats/geomstats/information_geometry/beta.py
imports: from scipy.stats import beta
  import geomstats.backend
  from geomstats.information_geometry.base import ScipyUnivariateRandomVariable
  from geomstats.information_geometry.dirichlet import DirichletDistributions, DirichletMetric
label_desc: Use image node_id 1 for calling the BetaMetric obj's underlying member method code with example usage: obj.metric_det(point) and returns: metric_det